accrue 1.3.0

A composable pipeline engine for structured data enrichment using LLMs

Accrue
The enrichment pipeline engine.

---

Define a pipeline. Point it at your data. Get structured results. Accrue is a Python library for enriching datasets with LLMs. Compose multi-step pipelines, run them across hundreds to tens of thousands of rows, and get validated, structured output back -- with caching, retries, and parallel execution handled for you.

No platform. No markup. Just a pipeline you can version-control, iterate on, and reason about.

from accrue import Pipeline, LLMStep

pipeline = Pipeline([
    LLMStep("analyze", fields={
        "market_size": "Estimate total addressable market in billions USD",
        "competition": {
            "prompt": "Rate competitive intensity with key competitors",
            "enum": ["Low", "Medium", "High"],
            "examples": ["High - Competes with AWS, Google Cloud"],
        },
        "growth_potential": {
            "prompt": "Assess 5-year growth trajectory",
            "type": "String",
            "format": "X% CAGR - reasoning",
        },
    })
])

result = pipeline.run(df)  # DataFrame in, DataFrame out
print(result.data.head())
print(f"Tokens used: {result.cost.total_tokens:,}")

Install

Requires Python 3.10+.

pip install accrue

Set your API key:

export OPENAI_API_KEY=sk-...

That's it. OpenAI is the default provider (zero config, structured outputs auto-enabled). Anthropic and Google are optional:

pip install accrue[anthropic]  # Claude
pip install accrue[google]     # Gemini

Claude Code Skill

If you use Claude Code, Accrue ships with a built-in /accrue skill that guides you through building pipelines interactively. It designs fields, picks models, estimates costs, and writes your script -- you just review and run.

> /accrue
> I have 500 companies in accounts.csv, I need to qualify them for ICP fit

The skill walks you through field design, model selection, pipeline architecture, and configuration before writing a production-ready script. See Using the Claude Code Skill for details.

Why Accrue

You have a spreadsheet of companies, leads, or entities. You need structured fields added to every row -- classifications, summaries, scores, extracted data. You could write a for loop and call the OpenAI API, but then you're building retry logic, rate limiting, caching, progress tracking, and crash recovery. You could use Clay, but you'd pay $500/month for something you can't version-control.

Accrue is the pipeline between a single API call and a full platform:

	Raw API calls	Accrue	Clay
Scope	One call at a time	Pipeline of steps across rows	Full SaaS platform
Multi-step	Manual orchestration	DAG with parallel execution	Sequential drag-and-drop
Caching	Build it yourself	SQLite, auto-invalidates on prompt change	Platform-managed
Crash recovery	Start over	Checkpoint + row-level cache resume	Platform-managed
Iterate on prompts	Re-run everything	Only re-process changed steps/rows	Re-run everything
Cost	API costs	API costs	$$$$/month + API costs
Version control	Yes	Yes	No

Quick Example

Chain steps together with depends_on. Use web_search() to ground LLM answers in live data:

from accrue import Pipeline, FunctionStep, LLMStep, web_search

pipeline = Pipeline([
    FunctionStep("research",
        fn=web_search("Research {company}: market position, competitors, recent news"),
        fields=["__web_context", "sources"],
    ),
    LLMStep("analyze",
        fields={
            "market_size": "Estimate TAM in billions USD",
            "competitors": {"prompt": "List top 3 competitors", "type": "List[String]"},
            "investment_thesis": "One-paragraph investment thesis",
        },
        depends_on=["research"],
    ),
])

result = pipeline.run(companies_df)

Features

• Multi-step pipelines -- Chain LLM steps and function steps into a DAG with automatic dependency resolution and parallel execution. Quickstart

• Provider-agnostic -- OpenAI, Anthropic (with automatic prompt caching), and Google ship as adapters. Any OpenAI-compatible API works via base_url. Custom providers implement one async method. Providers guide

• 7-key field specs -- Control LLM output with prompt, type, format, enum, examples, bad_examples, and default. Drives structured outputs and Pydantic validation automatically. Field specs guide

• Caching and checkpointing -- SQLite input-hash cache auto-invalidates on prompt changes. Checkpointing saves after each step for crash recovery. Caching guide

• Batch API -- LLMStep(batch=True) for 50% cost savings via OpenAI and Anthropic batch endpoints. Cache-aware, auto-chunking, realtime fallback on failures. Batch guide

• Web search and grounding -- web_search() factory for search-then-analyze pipelines, or grounding=True for native provider web search with normalized citations. Web search guide

• Conditional steps -- run_if / skip_if predicates for per-row branching. Skipped rows get defaults, never hit the API. Conditional steps guide

• Hooks -- Typed lifecycle events for observability. Sync and async callables, never crash the pipeline. Hooks guide

• provider_kwargs -- Escape hatch for provider-specific features (extended thinking, effort control, etc.) without waiting for first-class support.

Sweet Spot

Accrue is built for 100 to 50,000 rows -- too many for manual work or single-call tools, too few to justify big data infrastructure.

Rows	Time (3 steps, 10 workers)	Cost (gpt-4.1-mini)
100	~30s	~$0.20
1,000	~5 min	~$2
10,000	~50 min	~$20
50,000	~50 min (50 workers)	~$100

With batch=True, halve the API costs. Cached steps re-run in seconds.

Documentation

Section	Description
Getting Started	Installation, first pipeline, core concepts
Claude Code Skill	Interactive pipeline builder via /accrue
Guides	Field specs, providers, caching, batch API, grounding, hooks, errors, configuration
Cookbook	End-to-end examples: company enrichment, lead scoring, content analysis, batch processing
API Reference	Complete reference for every public export

Contributing

git clone https://github.com/matt-house-e/accrue.git
cd accrue
pip install -e ".[dev]"
pytest

License

MIT